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  <h1>Source code for torch.optim.optimizer</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span>
<span class="kn">from</span> <span class="nn">torch._six</span> <span class="kn">import</span> <span class="n">container_abcs</span>

<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span>
<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">chain</span>


<span class="k">class</span> <span class="nc">_RequiredParameter</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Singleton class representing a required parameter for an Optimizer.&quot;&quot;&quot;</span>
    <span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">return</span> <span class="s2">&quot;&lt;required parameter&gt;&quot;</span>

<span class="n">required</span> <span class="o">=</span> <span class="n">_RequiredParameter</span><span class="p">()</span>


<div class="viewcode-block" id="Optimizer"><a class="viewcode-back" href="../../../optim.html#torch.optim.Optimizer">[docs]</a><span class="k">class</span> <span class="nc">Optimizer</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Base class for all optimizers.</span>

<span class="sd">    .. warning::</span>
<span class="sd">        Parameters need to be specified as collections that have a deterministic</span>
<span class="sd">        ordering that is consistent between runs. Examples of objects that don&#39;t</span>
<span class="sd">        satisfy those properties are sets and iterators over values of dictionaries.</span>

<span class="sd">    Arguments:</span>
<span class="sd">        params (iterable): an iterable of :class:`torch.Tensor` s or</span>
<span class="sd">            :class:`dict` s. Specifies what Tensors should be optimized.</span>
<span class="sd">        defaults: (dict): a dict containing default values of optimization</span>
<span class="sd">            options (used when a parameter group doesn&#39;t specify them).</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">defaults</span><span class="p">):</span>
        <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_log_api_usage_once</span><span class="p">(</span><span class="s2">&quot;python.optimizer&quot;</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">defaults</span> <span class="o">=</span> <span class="n">defaults</span>

        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;params argument given to the optimizer should be &quot;</span>
                            <span class="s2">&quot;an iterable of Tensors or dicts, but got &quot;</span> <span class="o">+</span>
                            <span class="n">torch</span><span class="o">.</span><span class="n">typename</span><span class="p">(</span><span class="n">params</span><span class="p">))</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">state</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">dict</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">param_groups</span> <span class="o">=</span> <span class="p">[]</span>

        <span class="n">param_groups</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">params</span><span class="p">)</span>
        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">param_groups</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;optimizer got an empty parameter list&quot;</span><span class="p">)</span>
        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">param_groups</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">dict</span><span class="p">):</span>
            <span class="n">param_groups</span> <span class="o">=</span> <span class="p">[{</span><span class="s1">&#39;params&#39;</span><span class="p">:</span> <span class="n">param_groups</span><span class="p">}]</span>

        <span class="k">for</span> <span class="n">param_group</span> <span class="ow">in</span> <span class="n">param_groups</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">add_param_group</span><span class="p">(</span><span class="n">param_group</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">__getstate__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">return</span> <span class="p">{</span>
            <span class="s1">&#39;defaults&#39;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">defaults</span><span class="p">,</span>
            <span class="s1">&#39;state&#39;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="p">,</span>
            <span class="s1">&#39;param_groups&#39;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">param_groups</span><span class="p">,</span>
        <span class="p">}</span>

    <span class="k">def</span> <span class="nf">__setstate__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">state</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">state</span><span class="p">)</span>

    <span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="n">format_string</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span> <span class="o">+</span> <span class="s1">&#39; (&#39;</span>
        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">group</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">param_groups</span><span class="p">):</span>
            <span class="n">format_string</span> <span class="o">+=</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span>
            <span class="n">format_string</span> <span class="o">+=</span> <span class="s1">&#39;Parameter Group </span><span class="si">{0}</span><span class="se">\n</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
            <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">group</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
                <span class="k">if</span> <span class="n">key</span> <span class="o">!=</span> <span class="s1">&#39;params&#39;</span><span class="p">:</span>
                    <span class="n">format_string</span> <span class="o">+=</span> <span class="s1">&#39;    </span><span class="si">{0}</span><span class="s1">: </span><span class="si">{1}</span><span class="se">\n</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">group</span><span class="p">[</span><span class="n">key</span><span class="p">])</span>
        <span class="n">format_string</span> <span class="o">+=</span> <span class="s1">&#39;)&#39;</span>
        <span class="k">return</span> <span class="n">format_string</span>

<div class="viewcode-block" id="Optimizer.state_dict"><a class="viewcode-back" href="../../../optim.html#torch.optim.Optimizer.state_dict">[docs]</a>    <span class="k">def</span> <span class="nf">state_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the state of the optimizer as a :class:`dict`.</span>

<span class="sd">        It contains two entries:</span>

<span class="sd">        * state - a dict holding current optimization state. Its content</span>
<span class="sd">            differs between optimizer classes.</span>
<span class="sd">        * param_groups - a dict containing all parameter groups</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="c1"># Save ids instead of Tensors</span>
        <span class="k">def</span> <span class="nf">pack_group</span><span class="p">(</span><span class="n">group</span><span class="p">):</span>
            <span class="n">packed</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">v</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">group</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="n">k</span> <span class="o">!=</span> <span class="s1">&#39;params&#39;</span><span class="p">}</span>
            <span class="n">packed</span><span class="p">[</span><span class="s1">&#39;params&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="nb">id</span><span class="p">(</span><span class="n">p</span><span class="p">)</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">group</span><span class="p">[</span><span class="s1">&#39;params&#39;</span><span class="p">]]</span>
            <span class="k">return</span> <span class="n">packed</span>
        <span class="n">param_groups</span> <span class="o">=</span> <span class="p">[</span><span class="n">pack_group</span><span class="p">(</span><span class="n">g</span><span class="p">)</span> <span class="k">for</span> <span class="n">g</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">param_groups</span><span class="p">]</span>
        <span class="c1"># Remap state to use ids as keys</span>
        <span class="n">packed_state</span> <span class="o">=</span> <span class="p">{(</span><span class="nb">id</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="k">else</span> <span class="n">k</span><span class="p">):</span> <span class="n">v</span>
                        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
        <span class="k">return</span> <span class="p">{</span>
            <span class="s1">&#39;state&#39;</span><span class="p">:</span> <span class="n">packed_state</span><span class="p">,</span>
            <span class="s1">&#39;param_groups&#39;</span><span class="p">:</span> <span class="n">param_groups</span><span class="p">,</span>
        <span class="p">}</span></div>

<div class="viewcode-block" id="Optimizer.load_state_dict"><a class="viewcode-back" href="../../../optim.html#torch.optim.Optimizer.load_state_dict">[docs]</a>    <span class="k">def</span> <span class="nf">load_state_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">):</span>
        <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Loads the optimizer state.</span>

<span class="sd">        Arguments:</span>
<span class="sd">            state_dict (dict): optimizer state. Should be an object returned</span>
<span class="sd">                from a call to :meth:`state_dict`.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="c1"># deepcopy, to be consistent with module API</span>
        <span class="n">state_dict</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">state_dict</span><span class="p">)</span>
        <span class="c1"># Validate the state_dict</span>
        <span class="n">groups</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">param_groups</span>
        <span class="n">saved_groups</span> <span class="o">=</span> <span class="n">state_dict</span><span class="p">[</span><span class="s1">&#39;param_groups&#39;</span><span class="p">]</span>

        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">groups</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">saved_groups</span><span class="p">):</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;loaded state dict has a different number of &quot;</span>
                             <span class="s2">&quot;parameter groups&quot;</span><span class="p">)</span>
        <span class="n">param_lens</span> <span class="o">=</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">g</span><span class="p">[</span><span class="s1">&#39;params&#39;</span><span class="p">])</span> <span class="k">for</span> <span class="n">g</span> <span class="ow">in</span> <span class="n">groups</span><span class="p">)</span>
        <span class="n">saved_lens</span> <span class="o">=</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">g</span><span class="p">[</span><span class="s1">&#39;params&#39;</span><span class="p">])</span> <span class="k">for</span> <span class="n">g</span> <span class="ow">in</span> <span class="n">saved_groups</span><span class="p">)</span>
        <span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="n">p_len</span> <span class="o">!=</span> <span class="n">s_len</span> <span class="k">for</span> <span class="n">p_len</span><span class="p">,</span> <span class="n">s_len</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">param_lens</span><span class="p">,</span> <span class="n">saved_lens</span><span class="p">)):</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;loaded state dict contains a parameter group &quot;</span>
                             <span class="s2">&quot;that doesn&#39;t match the size of optimizer&#39;s group&quot;</span><span class="p">)</span>

        <span class="c1"># Update the state</span>
        <span class="n">id_map</span> <span class="o">=</span> <span class="p">{</span><span class="n">old_id</span><span class="p">:</span> <span class="n">p</span> <span class="k">for</span> <span class="n">old_id</span><span class="p">,</span> <span class="n">p</span> <span class="ow">in</span>
                  <span class="nb">zip</span><span class="p">(</span><span class="n">chain</span><span class="p">(</span><span class="o">*</span><span class="p">(</span><span class="n">g</span><span class="p">[</span><span class="s1">&#39;params&#39;</span><span class="p">]</span> <span class="k">for</span> <span class="n">g</span> <span class="ow">in</span> <span class="n">saved_groups</span><span class="p">)),</span>
                      <span class="n">chain</span><span class="p">(</span><span class="o">*</span><span class="p">(</span><span class="n">g</span><span class="p">[</span><span class="s1">&#39;params&#39;</span><span class="p">]</span> <span class="k">for</span> <span class="n">g</span> <span class="ow">in</span> <span class="n">groups</span><span class="p">)))}</span>

        <span class="k">def</span> <span class="nf">cast</span><span class="p">(</span><span class="n">param</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
            <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Make a deep copy of value, casting all tensors to device of param.&quot;&quot;&quot;</span>
            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
                <span class="c1"># Floating-point types are a bit special here. They are the only ones</span>
                <span class="c1"># that are assumed to always match the type of params.</span>
                <span class="k">if</span> <span class="n">param</span><span class="o">.</span><span class="n">is_floating_point</span><span class="p">():</span>
                    <span class="n">value</span> <span class="o">=</span> <span class="n">value</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">param</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
                <span class="n">value</span> <span class="o">=</span> <span class="n">value</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">param</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
                <span class="k">return</span> <span class="n">value</span>
            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
                <span class="k">return</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">cast</span><span class="p">(</span><span class="n">param</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">value</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">container_abcs</span><span class="o">.</span><span class="n">Iterable</span><span class="p">):</span>
                <span class="k">return</span> <span class="nb">type</span><span class="p">(</span><span class="n">value</span><span class="p">)(</span><span class="n">cast</span><span class="p">(</span><span class="n">param</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">value</span><span class="p">)</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="k">return</span> <span class="n">value</span>

        <span class="c1"># Copy state assigned to params (and cast tensors to appropriate types).</span>
        <span class="c1"># State that is not assigned to params is copied as is (needed for</span>
        <span class="c1"># backward compatibility).</span>
        <span class="n">state</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">dict</span><span class="p">)</span>
        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">state_dict</span><span class="p">[</span><span class="s1">&#39;state&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
            <span class="k">if</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">id_map</span><span class="p">:</span>
                <span class="n">param</span> <span class="o">=</span> <span class="n">id_map</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>
                <span class="n">state</span><span class="p">[</span><span class="n">param</span><span class="p">]</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="n">param</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="n">state</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span>

        <span class="c1"># Update parameter groups, setting their &#39;params&#39; value</span>
        <span class="k">def</span> <span class="nf">update_group</span><span class="p">(</span><span class="n">group</span><span class="p">,</span> <span class="n">new_group</span><span class="p">):</span>
            <span class="n">new_group</span><span class="p">[</span><span class="s1">&#39;params&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">group</span><span class="p">[</span><span class="s1">&#39;params&#39;</span><span class="p">]</span>
            <span class="k">return</span> <span class="n">new_group</span>
        <span class="n">param_groups</span> <span class="o">=</span> <span class="p">[</span>
            <span class="n">update_group</span><span class="p">(</span><span class="n">g</span><span class="p">,</span> <span class="n">ng</span><span class="p">)</span> <span class="k">for</span> <span class="n">g</span><span class="p">,</span> <span class="n">ng</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">groups</span><span class="p">,</span> <span class="n">saved_groups</span><span class="p">)]</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">__setstate__</span><span class="p">({</span><span class="s1">&#39;state&#39;</span><span class="p">:</span> <span class="n">state</span><span class="p">,</span> <span class="s1">&#39;param_groups&#39;</span><span class="p">:</span> <span class="n">param_groups</span><span class="p">})</span></div>

<div class="viewcode-block" id="Optimizer.zero_grad"><a class="viewcode-back" href="../../../optim.html#torch.optim.Optimizer.zero_grad">[docs]</a>    <span class="k">def</span> <span class="nf">zero_grad</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Clears the gradients of all optimized :class:`torch.Tensor` s.&quot;&quot;&quot;</span>
        <span class="k">for</span> <span class="n">group</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">param_groups</span><span class="p">:</span>
            <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">group</span><span class="p">[</span><span class="s1">&#39;params&#39;</span><span class="p">]:</span>
                <span class="k">if</span> <span class="n">p</span><span class="o">.</span><span class="n">grad</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
                    <span class="n">p</span><span class="o">.</span><span class="n">grad</span><span class="o">.</span><span class="n">detach_</span><span class="p">()</span>
                    <span class="n">p</span><span class="o">.</span><span class="n">grad</span><span class="o">.</span><span class="n">zero_</span><span class="p">()</span></div>

<div class="viewcode-block" id="Optimizer.step"><a class="viewcode-back" href="../../../optim.html#torch.optim.Optimizer.step">[docs]</a>    <span class="k">def</span> <span class="nf">step</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">closure</span><span class="p">):</span>
        <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Performs a single optimization step (parameter update).</span>

<span class="sd">        Arguments:</span>
<span class="sd">            closure (callable): A closure that reevaluates the model and</span>
<span class="sd">                returns the loss. Optional for most optimizers.</span>

<span class="sd">        .. note::</span>
<span class="sd">            Unless otherwise specified, this function should not modify the</span>
<span class="sd">            ``.grad`` field of the parameters.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">raise</span> <span class="ne">NotImplementedError</span></div>

<div class="viewcode-block" id="Optimizer.add_param_group"><a class="viewcode-back" href="../../../optim.html#torch.optim.Optimizer.add_param_group">[docs]</a>    <span class="k">def</span> <span class="nf">add_param_group</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">param_group</span><span class="p">):</span>
        <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Add a param group to the :class:`Optimizer` s `param_groups`.</span>

<span class="sd">        This can be useful when fine tuning a pre-trained network as frozen layers can be made</span>
<span class="sd">        trainable and added to the :class:`Optimizer` as training progresses.</span>

<span class="sd">        Arguments:</span>
<span class="sd">            param_group (dict): Specifies what Tensors should be optimized along with group</span>
<span class="sd">            specific optimization options.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">param_group</span><span class="p">,</span> <span class="nb">dict</span><span class="p">),</span> <span class="s2">&quot;param group must be a dict&quot;</span>

        <span class="n">params</span> <span class="o">=</span> <span class="n">param_group</span><span class="p">[</span><span class="s1">&#39;params&#39;</span><span class="p">]</span>
        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
            <span class="n">param_group</span><span class="p">[</span><span class="s1">&#39;params&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">params</span><span class="p">]</span>
        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="nb">set</span><span class="p">):</span>
            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;optimizer parameters need to be organized in ordered collections, but &#39;</span>
                            <span class="s1">&#39;the ordering of tensors in sets will change between runs. Please use a list instead.&#39;</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">param_group</span><span class="p">[</span><span class="s1">&#39;params&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">params</span><span class="p">)</span>

        <span class="k">for</span> <span class="n">param</span> <span class="ow">in</span> <span class="n">param_group</span><span class="p">[</span><span class="s1">&#39;params&#39;</span><span class="p">]:</span>
            <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">param</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;optimizer can only optimize Tensors, &quot;</span>
                                <span class="s2">&quot;but one of the params is &quot;</span> <span class="o">+</span> <span class="n">torch</span><span class="o">.</span><span class="n">typename</span><span class="p">(</span><span class="n">param</span><span class="p">))</span>
            <span class="k">if</span> <span class="ow">not</span> <span class="n">param</span><span class="o">.</span><span class="n">is_leaf</span><span class="p">:</span>
                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;can&#39;t optimize a non-leaf Tensor&quot;</span><span class="p">)</span>

        <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">default</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">defaults</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
            <span class="k">if</span> <span class="n">default</span> <span class="ow">is</span> <span class="n">required</span> <span class="ow">and</span> <span class="n">name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">param_group</span><span class="p">:</span>
                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;parameter group didn&#39;t specify a value of required optimization parameter &quot;</span> <span class="o">+</span>
                                 <span class="n">name</span><span class="p">)</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="n">param_group</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">default</span><span class="p">)</span>

        <span class="n">param_set</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
        <span class="k">for</span> <span class="n">group</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">param_groups</span><span class="p">:</span>
            <span class="n">param_set</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">group</span><span class="p">[</span><span class="s1">&#39;params&#39;</span><span class="p">]))</span>

        <span class="k">if</span> <span class="ow">not</span> <span class="n">param_set</span><span class="o">.</span><span class="n">isdisjoint</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">param_group</span><span class="p">[</span><span class="s1">&#39;params&#39;</span><span class="p">])):</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;some parameters appear in more than one parameter group&quot;</span><span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">param_groups</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">param_group</span><span class="p">)</span></div></div>
</pre></div>

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